The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor reaching an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical structures such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan; it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
One general method of automatic image processing employs feature based recognition techniques to determine the presence of anatomical structures in medical images. However, feature based recognition techniques can suffer from accuracy problems.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible abnormalities for further review. Such possible abnormalities are often called candidates and are considered to be generated by the CAD system based upon the medical images.
Computer Assisted Detection (CAD) assists medical professionals in diagnosis of patients based on data. For example, CAD can assist in identifying and/or diagnosing suspect locations in a medical image. CAD may operate as a first or second reviewer, e.g., providing more efficient and reliable second review. One use case is that CAD can be used for computed tomography (CT) colonography.
In CT colonography an interior view of the colon (i.e., the large intestine) is obtained using CT scanning. CT colonography allows viewing of the colon without a more invasive procedure where an endoscope is inserted into the rectum. CT colonography is a valuable tool for early detection of colon polyps that may later turn into cancer. From CT acquisitions of the patient's abdomen, radiologists are able to find polyps attached to the colon wall by inspecting two-dimensional reconstructions of individual planes of the image in different orientations or by performing a “virtual colonoscopy.” For virtual colonoscopy, a virtual fly-through of the entire interior of the colon is performed. The fly-through is from the rectum to the cecum, much in a way that would mimic an optical colonoscopy. However, virtual fly-through may be time consuming.
CAD systems are used as a second-reader after the fly-through to provide pointers to locations that the system determines to be polypoid in morphology and texture. The user enables the CAD marks after an initial unaided evaluation of the CT images. When enabled, the application shows marks labeled with an alpha-numeric code which appear both on a global three-dimensional view of the colon with arrows pointing to individual locations and as a list. Upon selection of one of the marks, the application automatically jumps to the corresponding location for the radiologist to evaluate. FIG. 1 shows an example of a CAD mark labeled 1a in a global three-dimensional view of the colon. When the user selects the marker 1a from the list of available CAD marks, the application sets the virtual endoscopic camera to the location of the CAD mark and sets the cross-bars of the two-dimensional reconstructions at the same location. Two-dimensional reconstructions and a three-dimensional endoscopic view are then rendered as shown in FIG. 2. However, this rendering may be time consuming. For a user to review all the candidates requires a substantial number of selections and then renderings, resulting in further delays.